Hadoop is not a type of database, but rather a software ecosystem that allows for massively parallel computing. It is an enabler of certain types NoSQL distributed databases, which can allow spread of data across thousands of servers (cluster) with little reduction in performance.
There has been a lot of talk about NoSQL over the past few years, but most people still do not know the difference between NoSQL and SQL. Understanding the differences and the benefits and drawbacks of each database model will help you make informed decisions.
1. Whereas SQL databases are relational, hence the name relational database management system (RDBMS), NoSQL database is non-relational or distributed. SQL databases are table-based whereas NoSQL databases are document-based, graph databases, wide-column stores, or key-value pairs. The structured query language is actually where SQL (Structured Query Language) gets its name. In NoSQL DB, focus for queries is on document collection. This is sometimes called Unstructured Query Language (UnQL). UnQL syntax varies from one database to the next.
2. In SQL database the data is in the form of tables consisting of a number of rows, whereas data in NoSQL has no standard schema definitions that it has to adhere to. NoSQL DBs have dynamic schema while SQL databases consist of predefined schema.
3. NoSQL DBs are horizontally scalable while SQL DBs are vertically scalable. To scale NoSQL DBs, increase the DB servers in the cluster for load balancing. To scale SQL DBs, increase the horsepower of the CPU, SSD, RAM and other hardware on the server. This means NoSQL is the best option if scalability is a major consideration.
4. SQL allows for interaction since it is a declarative query language. Once you state what you want such as to display, the DB extracts the results after assembling an algorithm internally. With NoSQL, MapReduce, being a procedural query technique, requires that you not only know what you want, but that you state exactly how to produce the answer. The increased interaction with data allows for new insight that will help with product development.
5. SQL has been around for a while and this explains why it is standardised. Although some vendors introduce dialects to their interfaces, the core is standardised and additional specs like JDBC and ODBC provide stable and broadly available SQL stores interfaces. This allows for an ecosystem of operator and management tools to help in the designing, monitoring, inspection, exploration, and building of applications on SQL systems. This means SQL programmers and users can reuse their UI knowledge and API across different backend systems, thereby reducing the development time of applications. Standardisation is also important in that it allows declarative third-party ETL (Extract, Transform, Load) tools. These tools enable you to flow data across systems and between databases.
[easy-tweet tweet=”Big data facilitates predictive analysis, which will keep you one step ahead of your competitors.” hashtags=”bigdata”]
6. SQL DBs are a better fit for complex query-intensive environments. This is because NoSQL does not have a standard interface to perform complex queries on a high-level and queries on NoSQL are not as powerful as SQL queries.
7. NoSQL DBs are the better fit for hierarchical data storage. This is because NoSQL follows the key-value pair storage method that is similar to JSON data. This makes NoSQL the best option for big data since today, most SQL vendors have added JSON-type support as well as XML document support.
8. While it is possible to use NoSQL for average transactions, SQL is the best choice for heavy duty transactional-type applications. This is because NoSQL is not stable enough when loaded with complex transactional applications and high traffic. SQL are a good fit because its database is stable and it promises integrity and atomicity of data. This is mostly because NoSQL has not been around for as long as SQL.
9. You will get better support with SQL databases, mostly because SQL DBs have been around for longer. When you hire a remote DBA experts team, chances are that most of the DBAs have training in SQL DBs. However, more and more remote DBA teams today have experience with NoSQL because of the increased demand. SQL DBs are also more reliable since they have been tried and tested over several years.
10. With SQL DBs, emphasis is on Atomicity, Consistency, Isolation and Durability (ACID) properties. On the other hand, NoSQL DBs follows the Brewers Consistency, Availability and Partition (CAP) theorem.
11. SQL databases are either close-sourced from commercial vendors or open-source whereas NoSQLs are open-source. This means NoSQL is the better choice if you want to save money.
Popular examples of SQL databases are Oracle, MySQL, MS-SQL, Sqlite, and Postgres while popular examples of NoSQL are MongoDB, HBase, Redis, BigTable, RavenDb, CouchDb, Cassandra, and Neo4j.
Why Big Data?
Big data solutions are applicable for the processing and analysis of big data. Major sources of big data are black box data, power grid data, social media data, stock exchange data, transport data, and search engine data. You should go for big data management solutions, be they SQL or NoSQL, because:
1. Big data analysis allows you to get customer information that will allow you to treat your customers as individuals. Today’s customers are very demanding and they want to connect one-on-one and to do so in real time. Information on what customers are complaining about will also help you in reputation management.
2. With big data, you can test different variations of CAD (computer-aided design) images. The information you get will help you determine how a change will affect your product and processes.
3. Big data facilitates predictive analysis, which will keep you one step ahead of your competitors. You will be able to analyse social media feeds as well as newspaper reports to get information that will help you take advantage of certain opportunities. Big data also allows you to do health-tests on customers and suppliers, thereby reducing defaulting rates.
4. Big data analysis will keep your data safe since there are tools to help map data landscape of a company for purposes of analysing internal threats. As an example, you will be able to flag emailing and the storage of 16 digit numbers (credit card numbers have 16 digits).
5. You will be able to diversify your revenue streams with big data since you will get trend data that will help you come up with new revenue streams.
The above article will let you know about the differences, benefits and drawbacks of each database model and why should you go should go for big data management solutions.